Computers are awesome, but they don’t know how to do much on their own; you have to train them. Crowdsourcing turns out to be a great way to do this. Suppose you would like to have an algorithm to measure something — like whether a tweet about a movie is positive or negative. You might want to know this so you can count positive and negative tweets about a particular movie and use that information to predict box office success (like Asur and Huberman do in this paper). You could try and think of all of the positive and negative words that you know and then only count tweets that include those words, but you’d probably miss a lot. You could categorize all of the tweets yourself, or hire a student to do it, but by the time you finished the movie would be on late night cable TV. You need a computer algorithm so you can pull thousands of tweets and count them quick, but a computer just doesn’t know the difference between a positive tweet and negative tweet until you train it.
That’s where the crowd comes in. People can easily judge the tone of a tweet, and you don’t have to be an expert to do it. So, what you can do is gather a pile of tweets — say a few thousand — put them up on Amazon Mechanical Turk, and let the crowd label them as positive or negative. At a few cents per tweet you can do this for something in the ballpark of a hundred bucks. Now that you have a pile of labeled tweets, you can train the computer. There’s lots of fancy terms for it — language model classifiers, self organizing fuzzy neural networks, … — but basically, you run a regression. The independent variable is stuff the computer can measure, like how many times certain words appear, and the dependent variable is whether the tweet is positive or negative. You estimate the regression (a.k.a train the classifier) on the tweets labeled by the crowd, and now you have an algorithm that can label new tweets that the crowd hasn’t labeled.When the next movie is coming out, you harvest the unlabeled tweets and feed them through the computer to see how many are positive and negative.
This is exactly how Hany Farid at Dartmouth trained his algorithm for detecting how much digital photographs have been altered. On it’s own the computer can measure lots of fancy statistical features of the image, but judging how significant the alteration of the image is requires a human. So, he gave lots of pairs of original and altered images to people on MTurk and had them rate how altered the images were. Then he essentially let the computer figure out what image characteristics for the altered images correlate with high alteration scores (but in a much fancier way then just a regular regression). Now, he has a trained algorithm that can read in photographs where we don’t have the original and predict how altered the image is.